With advances in medical technology and medical instruments, many abdominal surgical operations are being performed using a laparoscope. Since laparoscopic surgery is performed by viewing a three-dimensional object displayed on a two-dimensional image display device, training is indispensable for acquiring of the required skill. In actual laparoscopic surgery, the surgery must be planned so as to match each individual patient because the number of blood vessels, the positions of the blood vessels, and the positional relationship of organs, for example, the position and size of a tumor, differ from patient to patient.
For this purpose, it may be appropriate to perform, prior to surgery, a surgical simulation based on information acquired of each individual patient.
To acquire information of each individual patient, it is common to use medical image data such as CT or MRI data, but images of the membrane tissues surrounding the organ to be operated on cannot be captured by such means. Because of the inability to recognize such membrane tissues, there arises the problem that the membrane tissues cannot be modeled. A model that does not incorporate membrane tissues is unsuitable for use in a preoperative simulation. On the other hand, to compute the motion of an organ model at high speed, the physical and dynamic conditions of the model of the organ to be operated on may be set linearly. However, in this case, the deformation of the organ model would greatly differ from the actual deformation, rendering such a model unsatisfactory for use in a preoperative simulation.
Further, such a surgical simulator is equipped with a force sensing device that produces the reaction of a simulated organ that matches the position of the simulated surgical instrument being manipulated by the surgical simulation operator and the position where it touches the simulated organ. However, it is not common to compute the reaction of the simulated organ and supply the computed reaction to the force sensing device, while at the same time, computing in real time the position achieved by the motion of the simulated organ.
Further, in a prior art surgical simulation model of a simulated organ that uses a finite-element method, volume data for an organ, for example, is meshed to generate a simulated organ segmented into a plurality of tetrahedrons. Then, a stiffness matrix that describes the dynamic property of the simulated organ is generated by applying Young's modulus or Poisson's ratio or the like as physical values to the tetrahedrons. Then, the motion equation of the simulated organ that uses the stiffness matrix is solved by numerical computation, thereby simulating the motion of the simulated organ.
However, since it takes a finite time to complete the numerical computation of the motion equation that uses such a stiffness matrix, it has not been possible to compute the motion of the simulated organ in real time. Furthermore, the computation using the prior art stiffness matrix has had the problem that the computation may diverge.